Point-to-point ILC with Accelerated Convergence

被引：0

作者：

Chu, Bing ^{[1
]}

Owens, David H. ^{[2
,3
]}

Freeman, Chris T. ^{[1
]}

Liu, Yanhong ^{[2
]}

机构：

[1] Univ Southampton, Southampton SO17 1BJ, Hants, England

[2] Zhengzhou Univ, Zhengzhou 450001, Henan, Peoples R China

[3] Univ Sheffield, Sheffield S1 3JD, S Yorkshire, England

来源：

2017 6TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS (DDCLS) | 2017年

关键词：

Point-to-point terative learning control; Successive projection; Accelerated convergence; ITERATIVE LEARNING CONTROL; RESIDUAL VIBRATION SUPPRESSION; TRACKING; SYSTEMS; ALGORITHMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a novel point-to-point iterative learning control (ILC) algorithm for high performance trajectory tracking applications. Based on a successive project formulation of the point-to-point ILC design problem, two point-to-point ILC design algorithms are derived: one algorithm recovers the norm optimal point to point ILC algorithm with a desirable physical property of converging to the minimum norm (energy) solution, and the other one (interestingly) accelerates convergence speed which could lead to significant reduction in system configuration time/cost. Numerical results are provided to demonstrate the proposed algorithms' effectiveness.

引用

页码：530 / 535

页数：6

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